ALwrity version 0.5.5
This commit is contained in:
ajaysi
@@ -332,7 +332,7 @@ async def generate_comprehensive_strategy_polling(
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"onboarding_data": onboarding_data,
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"user_id": user_id,
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"generation_config": config or {}
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}
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}
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# Create strategy generation config
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generation_config = StrategyGenerationConfig(
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@@ -26,6 +26,8 @@ class AutoFillRefreshService:
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- Optionally augments with AI overrides (hook, not persisted)
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- Returns payload in the same shape as AutoFillService.get_autofill, plus meta
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"""
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logger.info(f"AutoFillRefreshService: starting build_fresh_payload | user=%s | use_ai=%s | ai_only=%s", user_id, use_ai, ai_only)
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# Base context from onboarding analysis (used for AI context only when ai_only)
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logger.debug("AutoFillRefreshService: processing onboarding context | user=%s", user_id)
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base_context = await self.autofill.integration.process_onboarding_data(user_id, self.db)
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@@ -37,6 +39,33 @@ class AutoFillRefreshService:
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bool((base_context or {}).get('api_keys_data')),
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bool((base_context or {}).get('onboarding_session')),
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)
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# Log detailed context analysis
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logger.info(f"AutoFillRefreshService: detailed context analysis | user=%s", user_id)
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if base_context:
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website_analysis = base_context.get('website_analysis', {})
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research_preferences = base_context.get('research_preferences', {})
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api_keys_data = base_context.get('api_keys_data', {})
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onboarding_session = base_context.get('onboarding_session', {})
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logger.info(f" - Website analysis keys: {list(website_analysis.keys()) if website_analysis else 'None'}")
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logger.info(f" - Research preferences keys: {list(research_preferences.keys()) if research_preferences else 'None'}")
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logger.info(f" - API keys data keys: {list(api_keys_data.keys()) if api_keys_data else 'None'}")
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logger.info(f" - Onboarding session keys: {list(onboarding_session.keys()) if onboarding_session else 'None'}")
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# Log specific data points
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if website_analysis:
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logger.info(f" - Website URL: {website_analysis.get('website_url', 'Not found')}")
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logger.info(f" - Website status: {website_analysis.get('status', 'Unknown')}")
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if research_preferences:
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logger.info(f" - Research depth: {research_preferences.get('research_depth', 'Not found')}")
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logger.info(f" - Content types: {research_preferences.get('content_types', 'Not found')}")
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if api_keys_data:
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logger.info(f" - API providers: {api_keys_data.get('providers', [])}")
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logger.info(f" - Total keys: {api_keys_data.get('total_keys', 0)}")
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else:
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logger.warning(f"AutoFillRefreshService: no base context available | user=%s", user_id)
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try:
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w = (base_context or {}).get('website_analysis') or {}
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r = (base_context or {}).get('research_preferences') or {}
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@@ -50,6 +79,16 @@ class AutoFillRefreshService:
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ai_payload = await self.structured_ai.generate_autofill_fields(user_id, base_context)
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meta = ai_payload.get('meta') or {}
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logger.info("AI-only payload meta: ai_used=%s overrides=%s", meta.get('ai_used'), meta.get('ai_overrides_count'))
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# Log detailed AI payload analysis
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logger.info(f"AutoFillRefreshService: AI payload analysis | user=%s", user_id)
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logger.info(f" - AI used: {meta.get('ai_used', False)}")
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logger.info(f" - AI overrides count: {meta.get('ai_overrides_count', 0)}")
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logger.info(f" - Success rate: {meta.get('success_rate', 0):.1f}%")
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logger.info(f" - Attempts: {meta.get('attempts', 0)}")
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logger.info(f" - Missing fields: {len(meta.get('missing_fields', []))}")
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logger.info(f" - Fields generated: {len(ai_payload.get('fields', {}))}")
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return ai_payload
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except Exception as e:
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logger.error("AI-only structured generation failed | user=%s | err=%s", user_id, repr(e))
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@@ -68,6 +107,7 @@ class AutoFillRefreshService:
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}
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# Fallback to previous behavior (DB + sparse overrides)
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logger.info("AutoFillRefreshService: using fallback behavior (DB + sparse overrides)")
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payload = await self.autofill.get_autofill(user_id)
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logger.info("AutoFillRefreshService: Base payload fields: %d", len(payload.get('fields', {})))
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@@ -496,10 +496,21 @@ Generate the complete JSON with all 30 fields personalized for {website_url}:
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logger.info("AIStructuredAutofillService: generating %d fields | user=%s", len(CORE_FIELDS), user_id)
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logger.debug("AIStructuredAutofillService: properties=%d", len(schema.get('properties', {})))
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# Log context summary for debugging
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logger.info("AIStructuredAutofillService: context summary | user=%s", user_id)
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logger.info(" - Website analysis exists: %s", bool(context_summary.get('user_profile', {}).get('website_url')))
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logger.info(" - Research config: %s", context_summary.get('research_config', {}).get('research_depth', 'None'))
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logger.info(" - API capabilities: %s", len(context_summary.get('api_capabilities', {}).get('providers', [])))
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logger.info(" - Content analysis: %s", bool(context_summary.get('content_analysis')))
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logger.info(" - Audience insights: %s", bool(context_summary.get('audience_insights')))
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# Log prompt length for debugging
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logger.info("AIStructuredAutofillService: prompt length=%d chars | user=%s", len(prompt), user_id)
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last_result = None
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for attempt in range(self.max_retries + 1):
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try:
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logger.info(f"AI structured call attempt {attempt + 1}/{self.max_retries + 1}")
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logger.info(f"AI structured call attempt {attempt + 1}/{self.max_retries + 1} | user=%s", user_id)
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result = await self.ai.execute_structured_json_call(
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service_type=AIServiceType.STRATEGIC_INTELLIGENCE,
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prompt=prompt,
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@@ -507,8 +518,34 @@ Generate the complete JSON with all 30 fields personalized for {website_url}:
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)
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last_result = result
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# Log AI response details
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logger.info(f"AI response received | attempt={attempt + 1} | user=%s", user_id)
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if isinstance(result, dict):
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logger.info(f" - Response keys: {list(result.keys())}")
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logger.info(f" - Response type: dict with {len(result)} items")
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# Handle wrapped response from AI service manager
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if 'data' in result and 'success' in result:
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# This is a wrapped response from AI service manager
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if result.get('success'):
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# Extract the actual AI response from the 'data' field
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ai_response = result.get('data', {})
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logger.info(f" - Extracted AI response from wrapped response")
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logger.info(f" - AI response keys: {list(ai_response.keys()) if isinstance(ai_response, dict) else 'N/A'}")
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last_result = ai_response
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else:
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# AI service failed
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error_msg = result.get('error', 'Unknown AI service error')
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logger.error(f" - AI service failed: {error_msg}")
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last_result = {'error': error_msg}
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elif 'error' in result:
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logger.error(f" - AI returned error: {result['error']}")
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else:
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logger.warning(f" - Response type: {type(result)}")
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# Check if we should retry
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if not self._should_retry(result, attempt):
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if not self._should_retry(last_result, attempt):
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logger.info(f"Retry not needed | attempt={attempt + 1} | user=%s", user_id)
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break
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# Add a small delay before retry
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@@ -7,6 +7,7 @@ import logging
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from typing import Dict, Any, Optional, List
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from datetime import datetime, timedelta
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from sqlalchemy.orm import Session
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import traceback
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# Import database models
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from models.enhanced_strategy_models import (
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@@ -39,6 +40,13 @@ class OnboardingDataIntegrationService:
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api_keys_data = self._get_api_keys_data(user_id, db)
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onboarding_session = self._get_onboarding_session(user_id, db)
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# Log data source status
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logger.info(f"Data source status for user {user_id}:")
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logger.info(f" - Website analysis: {'✅ Found' if website_analysis else '❌ Missing'}")
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logger.info(f" - Research preferences: {'✅ Found' if research_preferences else '❌ Missing'}")
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logger.info(f" - API keys data: {'✅ Found' if api_keys_data else '❌ Missing'}")
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logger.info(f" - Onboarding session: {'✅ Found' if onboarding_session else '❌ Missing'}")
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# Process and integrate data
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integrated_data = {
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'website_analysis': website_analysis,
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@@ -49,6 +57,14 @@ class OnboardingDataIntegrationService:
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'processing_timestamp': datetime.utcnow().isoformat()
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}
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# Log data quality assessment
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data_quality = integrated_data['data_quality']
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logger.info(f"Data quality assessment for user {user_id}:")
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logger.info(f" - Completeness: {data_quality.get('completeness', 0):.2f}")
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logger.info(f" - Freshness: {data_quality.get('freshness', 0):.2f}")
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logger.info(f" - Relevance: {data_quality.get('relevance', 0):.2f}")
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logger.info(f" - Confidence: {data_quality.get('confidence', 0):.2f}")
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# Store integrated data
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await self._store_integrated_data(user_id, integrated_data, db)
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@@ -57,6 +73,7 @@ class OnboardingDataIntegrationService:
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except Exception as e:
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logger.error(f"Error processing onboarding data for user {user_id}: {str(e)}")
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logger.error("Traceback:\n%s", traceback.format_exc())
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return self._get_fallback_data()
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def _get_website_analysis(self, user_id: int, db: Session) -> Dict[str, Any]:
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@@ -7,7 +7,20 @@ import google.genai as genai
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from google.genai import types
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from dotenv import load_dotenv
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load_dotenv(Path('../../../.env'))
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# Fix the environment loading path - load from backend directory
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current_dir = Path(__file__).parent.parent # services directory
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backend_dir = current_dir.parent # backend directory
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env_path = backend_dir / '.env'
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if env_path.exists():
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load_dotenv(env_path)
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print(f"Loaded .env from: {env_path}")
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else:
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# Fallback to current directory
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load_dotenv()
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print(f"No .env found at {env_path}, using current directory")
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from loguru import logger
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logger.remove()
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logger.add(sys.stdout,
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@@ -31,14 +44,33 @@ import logging
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logging.basicConfig(level=logging.INFO, format='[%(asctime)s-%(levelname)s-%(module)s-%(lineno)d]- %(message)s')
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logger = logging.getLogger(__name__)
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def get_gemini_api_key() -> str:
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"""Get Gemini API key with proper error handling."""
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api_key = os.getenv('GEMINI_API_KEY')
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if not api_key:
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error_msg = "GEMINI_API_KEY environment variable is not set. Please set it in your .env file."
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logger.error(error_msg)
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raise ValueError(error_msg)
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# Validate API key format (basic check)
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if not api_key.startswith('AIza'):
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error_msg = "GEMINI_API_KEY appears to be invalid. It should start with 'AIza'."
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logger.error(error_msg)
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raise ValueError(error_msg)
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return api_key
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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_prompt):
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""" Common functiont to get response from gemini pro Text. """
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#FIXME: Include : https://github.com/google-gemini/cookbook/blob/main/quickstarts/rest/System_instructions_REST.ipynb
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try:
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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api_key = get_gemini_api_key()
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client = genai.Client(api_key=api_key)
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logger.info("✅ Gemini client initialized successfully")
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except Exception as err:
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logger.error(f"Failed to configure Gemini: {err}")
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raise
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logger.info(f"Temp: {temperature}, MaxTokens: {max_tokens}, TopP: {top_p}, N: {n}")
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# Set up AI model config
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generation_config = {
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@@ -121,20 +153,32 @@ async def test_gemini_api_key(api_key: str) -> tuple[bool, str]:
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tuple[bool, str]: A tuple containing (is_valid, message)
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"""
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try:
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# Validate API key format first
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if not api_key:
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return False, "API key is empty"
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if not api_key.startswith('AIza'):
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return False, "API key format appears invalid (should start with 'AIza')"
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# Configure Gemini with the provided key
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genai.configure(api_key=api_key)
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client = genai.Client(api_key=api_key)
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# Try to list models as a simple API test
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models = genai.list_models()
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models = client.models.list()
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# Check if Gemini Pro is available
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if any(model.name == "gemini-pro" for model in models):
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model_names = [model.name for model in models]
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logger.info(f"Available models: {model_names}")
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if any("gemini" in model_name.lower() for model_name in model_names):
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return True, "Gemini API key is valid"
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else:
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return False, "Gemini Pro model not available with this API key"
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return False, "No Gemini models available with this API key"
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except Exception as e:
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return False, f"Error testing Gemini API key: {str(e)}"
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error_msg = f"Error testing Gemini API key: {str(e)}"
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logger.error(error_msg)
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return False, error_msg
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def gemini_pro_text_gen(prompt, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048):
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"""
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@@ -151,18 +195,20 @@ def gemini_pro_text_gen(prompt, temperature=0.7, top_p=0.9, top_k=40, max_tokens
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str: The generated text completion
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"""
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try:
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# Configure the model
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model = genai.GenerativeModel('gemini-pro')
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# Get API key with proper error handling
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api_key = get_gemini_api_key()
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client = genai.Client(api_key=api_key)
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# Generate content
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response = model.generate_content(
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prompt,
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generation_config=genai.types.GenerationConfig(
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# Generate content using the new client
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response = client.models.generate_content(
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model='gemini-2.5-flash',
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contents=prompt,
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config=types.GenerateContentConfig(
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max_output_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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max_output_tokens=max_tokens,
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)
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),
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)
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# Return the generated text
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@@ -210,7 +256,10 @@ def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9,
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Generate structured JSON response using Google's Gemini Pro model.
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"""
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try:
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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# Get API key with proper error handling
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api_key = get_gemini_api_key()
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client = genai.Client(api_key=api_key)
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logger.info("✅ Gemini client initialized for structured JSON response")
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# Build config using official SDK schema type
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try:
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@@ -329,6 +378,10 @@ def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9,
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logger.error(f"Error parsing structured response: {e}")
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return {"error": f"Failed to parse JSON response: {e}", "raw_response": (response.text or '')}
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except ValueError as e:
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# API key related errors
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logger.error(f"API key error in Gemini Pro structured JSON generation: {e}")
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return {"error": str(e)}
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except Exception as e:
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logger.error(f"Error in Gemini Pro structured JSON generation: {e}")
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return {"error": str(e)}
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142
backend/test_env_check.py
Normal file
142
backend/test_env_check.py
Normal file
@@ -0,0 +1,142 @@
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#!/usr/bin/env python3
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"""
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Test script to check environment variables and API key loading.
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"""
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import os
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import sys
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from pathlib import Path
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# Add the backend directory to the Python path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from dotenv import load_dotenv
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def test_environment_loading():
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"""Test environment variable loading."""
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print("🔍 Testing environment variable loading...")
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# Check current working directory
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print(f"Current working directory: {os.getcwd()}")
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# Check if .env file exists in various locations
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possible_env_paths = [
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Path('.env'), # Current directory
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Path('../.env'), # Parent directory
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Path('../../.env'), # Grandparent directory
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Path('../../../.env'), # Great-grandparent directory
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Path('backend/.env'), # Backend directory
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]
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print("\n📁 Checking for .env files:")
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for env_path in possible_env_paths:
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if env_path.exists():
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print(f"✅ Found .env file: {env_path.absolute()}")
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else:
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print(f"❌ No .env file: {env_path.absolute()}")
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# Try to load .env from different locations
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print("\n🔄 Attempting to load .env files:")
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for env_path in possible_env_paths:
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if env_path.exists():
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print(f"Loading .env from: {env_path.absolute()}")
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load_dotenv(env_path)
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break
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else:
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print("⚠️ No .env file found, trying to load from current directory")
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load_dotenv()
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# Check environment variables
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print("\n🔑 Checking environment variables:")
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env_vars_to_check = [
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'GEMINI_API_KEY',
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'GOOGLE_API_KEY',
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'OPENAI_API_KEY',
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'DATABASE_URL',
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'SECRET_KEY'
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]
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for var in env_vars_to_check:
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value = os.getenv(var)
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if value:
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# Show first few characters for security
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masked_value = value[:8] + "..." if len(value) > 8 else "***"
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print(f"✅ {var}: {masked_value}")
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else:
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print(f"❌ {var}: Not set")
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# Test specific Gemini API key loading
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print("\n🤖 Testing Gemini API key loading:")
|
||||
gemini_key = os.getenv('GEMINI_API_KEY')
|
||||
if gemini_key:
|
||||
print(f"✅ GEMINI_API_KEY found: {gemini_key[:8]}...")
|
||||
|
||||
# Test if the key looks valid
|
||||
if len(gemini_key) > 20:
|
||||
print("✅ API key length looks valid")
|
||||
else:
|
||||
print("⚠️ API key seems too short")
|
||||
else:
|
||||
print("❌ GEMINI_API_KEY not found")
|
||||
|
||||
# Check alternative names
|
||||
alternative_keys = ['GOOGLE_API_KEY', 'GEMINI_KEY', 'GOOGLE_AI_API_KEY']
|
||||
for alt_key in alternative_keys:
|
||||
alt_value = os.getenv(alt_key)
|
||||
if alt_value:
|
||||
print(f"⚠️ Found alternative key {alt_key}: {alt_value[:8]}...")
|
||||
|
||||
return gemini_key is not None
|
||||
|
||||
def test_gemini_provider_import():
|
||||
"""Test importing the Gemini provider."""
|
||||
print("\n🧪 Testing Gemini provider import...")
|
||||
|
||||
try:
|
||||
from services.llm_providers.gemini_provider import gemini_structured_json_response
|
||||
print("✅ Successfully imported gemini_structured_json_response")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import Gemini provider: {e}")
|
||||
return False
|
||||
|
||||
def test_ai_service_manager_import():
|
||||
"""Test importing the AI service manager."""
|
||||
print("\n🧪 Testing AI service manager import...")
|
||||
|
||||
try:
|
||||
from services.ai_service_manager import AIServiceManager
|
||||
print("✅ Successfully imported AIServiceManager")
|
||||
|
||||
# Try to create an instance
|
||||
ai_manager = AIServiceManager()
|
||||
print("✅ Successfully created AIServiceManager instance")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import/create AI service manager: {e}")
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 Starting environment and API key validation tests")
|
||||
print("=" * 60)
|
||||
|
||||
# Test environment loading
|
||||
env_ok = test_environment_loading()
|
||||
|
||||
# Test imports
|
||||
gemini_import_ok = test_gemini_provider_import()
|
||||
ai_manager_ok = test_ai_service_manager_import()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("📊 Test Results Summary:")
|
||||
print(f"Environment loading: {'✅ PASS' if env_ok else '❌ FAIL'}")
|
||||
print(f"Gemini provider import: {'✅ PASS' if gemini_import_ok else '❌ FAIL'}")
|
||||
print(f"AI service manager: {'✅ PASS' if ai_manager_ok else '❌ FAIL'}")
|
||||
|
||||
if not env_ok:
|
||||
print("\n💡 To fix environment issues:")
|
||||
print("1. Create a .env file in the backend directory")
|
||||
print("2. Add your GEMINI_API_KEY to the .env file")
|
||||
print("3. Example: GEMINI_API_KEY=your_actual_api_key_here")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
463
backend/test_onboarding_data.py
Normal file
463
backend/test_onboarding_data.py
Normal file
@@ -0,0 +1,463 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to validate onboarding data existence in the database.
|
||||
This script checks if onboarding data exists for test users and validates the data flow.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import asyncio
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
# Add the backend directory to the Python path
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
from sqlalchemy.orm import Session
|
||||
from services.database import get_db_session
|
||||
from models.onboarding import OnboardingSession, WebsiteAnalysis, ResearchPreferences, APIKey
|
||||
from models.enhanced_strategy_models import OnboardingDataIntegration
|
||||
from api.content_planning.services.content_strategy.onboarding.data_integration import OnboardingDataIntegrationService
|
||||
from api.content_planning.services.content_strategy.autofill.ai_structured_autofill import AIStructuredAutofillService
|
||||
from services.ai_service_manager import AIServiceManager
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.StreamHandler(sys.stdout),
|
||||
logging.FileHandler('onboarding_test.log')
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class OnboardingDataValidator:
|
||||
"""Validator for onboarding data existence and quality."""
|
||||
|
||||
def __init__(self):
|
||||
self.db_session = get_db_session()
|
||||
self.data_integration_service = OnboardingDataIntegrationService()
|
||||
self.ai_service = AIStructuredAutofillService()
|
||||
self.ai_manager = AIServiceManager()
|
||||
|
||||
def test_database_connection(self) -> bool:
|
||||
"""Test database connection."""
|
||||
try:
|
||||
# Simple query to test connection
|
||||
from sqlalchemy import text
|
||||
result = self.db_session.execute(text("SELECT 1"))
|
||||
logger.info("✅ Database connection successful")
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Database connection failed: {e}")
|
||||
return False
|
||||
|
||||
def check_onboarding_sessions(self, user_ids: list = None) -> Dict[int, Dict[str, Any]]:
|
||||
"""Check onboarding sessions for given user IDs."""
|
||||
if user_ids is None:
|
||||
user_ids = [1, 2, 3] # Default test user IDs
|
||||
|
||||
results = {}
|
||||
|
||||
for user_id in user_ids:
|
||||
logger.info(f"🔍 Checking onboarding session for user {user_id}")
|
||||
|
||||
try:
|
||||
session = self.db_session.query(OnboardingSession).filter(
|
||||
OnboardingSession.user_id == user_id
|
||||
).order_by(OnboardingSession.updated_at.desc()).first()
|
||||
|
||||
if session:
|
||||
results[user_id] = {
|
||||
'session_exists': True,
|
||||
'session_id': session.id,
|
||||
'status': session.status,
|
||||
'progress': session.progress,
|
||||
'created_at': session.created_at.isoformat(),
|
||||
'updated_at': session.updated_at.isoformat(),
|
||||
'data': session.to_dict() if hasattr(session, 'to_dict') else str(session)
|
||||
}
|
||||
logger.info(f"✅ Onboarding session found for user {user_id}: {session.status}")
|
||||
else:
|
||||
results[user_id] = {
|
||||
'session_exists': False,
|
||||
'error': 'No onboarding session found'
|
||||
}
|
||||
logger.warning(f"❌ No onboarding session found for user {user_id}")
|
||||
|
||||
except Exception as e:
|
||||
results[user_id] = {
|
||||
'session_exists': False,
|
||||
'error': str(e)
|
||||
}
|
||||
logger.error(f"❌ Error checking onboarding session for user {user_id}: {e}")
|
||||
|
||||
return results
|
||||
|
||||
def check_website_analysis(self, user_ids: list = None) -> Dict[int, Dict[str, Any]]:
|
||||
"""Check website analysis data for given user IDs."""
|
||||
if user_ids is None:
|
||||
user_ids = [1, 2, 3]
|
||||
|
||||
results = {}
|
||||
|
||||
for user_id in user_ids:
|
||||
logger.info(f"🔍 Checking website analysis for user {user_id}")
|
||||
|
||||
try:
|
||||
# Get onboarding session first
|
||||
session = self.db_session.query(OnboardingSession).filter(
|
||||
OnboardingSession.user_id == user_id
|
||||
).order_by(OnboardingSession.updated_at.desc()).first()
|
||||
|
||||
if not session:
|
||||
results[user_id] = {
|
||||
'website_analysis_exists': False,
|
||||
'error': 'No onboarding session found'
|
||||
}
|
||||
continue
|
||||
|
||||
# Get website analysis
|
||||
website_analysis = self.db_session.query(WebsiteAnalysis).filter(
|
||||
WebsiteAnalysis.session_id == session.id
|
||||
).order_by(WebsiteAnalysis.updated_at.desc()).first()
|
||||
|
||||
if website_analysis:
|
||||
results[user_id] = {
|
||||
'website_analysis_exists': True,
|
||||
'analysis_id': website_analysis.id,
|
||||
'website_url': website_analysis.website_url,
|
||||
'status': website_analysis.status,
|
||||
'created_at': website_analysis.created_at.isoformat(),
|
||||
'updated_at': website_analysis.updated_at.isoformat(),
|
||||
'data_keys': list(website_analysis.to_dict().keys()) if hasattr(website_analysis, 'to_dict') else []
|
||||
}
|
||||
logger.info(f"✅ Website analysis found for user {user_id}: {website_analysis.website_url}")
|
||||
else:
|
||||
results[user_id] = {
|
||||
'website_analysis_exists': False,
|
||||
'error': 'No website analysis found'
|
||||
}
|
||||
logger.warning(f"❌ No website analysis found for user {user_id}")
|
||||
|
||||
except Exception as e:
|
||||
results[user_id] = {
|
||||
'website_analysis_exists': False,
|
||||
'error': str(e)
|
||||
}
|
||||
logger.error(f"❌ Error checking website analysis for user {user_id}: {e}")
|
||||
|
||||
return results
|
||||
|
||||
def check_research_preferences(self, user_ids: list = None) -> Dict[int, Dict[str, Any]]:
|
||||
"""Check research preferences data for given user IDs."""
|
||||
if user_ids is None:
|
||||
user_ids = [1, 2, 3]
|
||||
|
||||
results = {}
|
||||
|
||||
for user_id in user_ids:
|
||||
logger.info(f"🔍 Checking research preferences for user {user_id}")
|
||||
|
||||
try:
|
||||
# Get onboarding session first
|
||||
session = self.db_session.query(OnboardingSession).filter(
|
||||
OnboardingSession.user_id == user_id
|
||||
).order_by(OnboardingSession.updated_at.desc()).first()
|
||||
|
||||
if not session:
|
||||
results[user_id] = {
|
||||
'research_preferences_exists': False,
|
||||
'error': 'No onboarding session found'
|
||||
}
|
||||
continue
|
||||
|
||||
# Get research preferences
|
||||
research_prefs = self.db_session.query(ResearchPreferences).filter(
|
||||
ResearchPreferences.session_id == session.id
|
||||
).first()
|
||||
|
||||
if research_prefs:
|
||||
results[user_id] = {
|
||||
'research_preferences_exists': True,
|
||||
'prefs_id': research_prefs.id,
|
||||
'research_depth': research_prefs.research_depth,
|
||||
'content_types': research_prefs.content_types,
|
||||
'created_at': research_prefs.created_at.isoformat(),
|
||||
'updated_at': research_prefs.updated_at.isoformat(),
|
||||
'data_keys': list(research_prefs.to_dict().keys()) if hasattr(research_prefs, 'to_dict') else []
|
||||
}
|
||||
logger.info(f"✅ Research preferences found for user {user_id}: {research_prefs.research_depth}")
|
||||
else:
|
||||
results[user_id] = {
|
||||
'research_preferences_exists': False,
|
||||
'error': 'No research preferences found'
|
||||
}
|
||||
logger.warning(f"❌ No research preferences found for user {user_id}")
|
||||
|
||||
except Exception as e:
|
||||
results[user_id] = {
|
||||
'research_preferences_exists': False,
|
||||
'error': str(e)
|
||||
}
|
||||
logger.error(f"❌ Error checking research preferences for user {user_id}: {e}")
|
||||
|
||||
return results
|
||||
|
||||
def check_api_keys(self, user_ids: list = None) -> Dict[int, Dict[str, Any]]:
|
||||
"""Check API keys data for given user IDs."""
|
||||
if user_ids is None:
|
||||
user_ids = [1, 2, 3]
|
||||
|
||||
results = {}
|
||||
|
||||
for user_id in user_ids:
|
||||
logger.info(f"🔍 Checking API keys for user {user_id}")
|
||||
|
||||
try:
|
||||
# Get onboarding session first
|
||||
session = self.db_session.query(OnboardingSession).filter(
|
||||
OnboardingSession.user_id == user_id
|
||||
).order_by(OnboardingSession.updated_at.desc()).first()
|
||||
|
||||
if not session:
|
||||
results[user_id] = {
|
||||
'api_keys_exist': False,
|
||||
'error': 'No onboarding session found'
|
||||
}
|
||||
continue
|
||||
|
||||
# Get API keys
|
||||
api_keys = self.db_session.query(APIKey).filter(
|
||||
APIKey.session_id == session.id
|
||||
).all()
|
||||
|
||||
if api_keys:
|
||||
results[user_id] = {
|
||||
'api_keys_exist': True,
|
||||
'count': len(api_keys),
|
||||
'providers': [key.provider for key in api_keys],
|
||||
'created_at': api_keys[0].created_at.isoformat() if api_keys else None,
|
||||
'updated_at': api_keys[0].updated_at.isoformat() if api_keys else None
|
||||
}
|
||||
logger.info(f"✅ API keys found for user {user_id}: {len(api_keys)} keys")
|
||||
else:
|
||||
results[user_id] = {
|
||||
'api_keys_exist': False,
|
||||
'error': 'No API keys found'
|
||||
}
|
||||
logger.warning(f"❌ No API keys found for user {user_id}")
|
||||
|
||||
except Exception as e:
|
||||
results[user_id] = {
|
||||
'api_keys_exist': False,
|
||||
'error': str(e)
|
||||
}
|
||||
logger.error(f"❌ Error checking API keys for user {user_id}: {e}")
|
||||
|
||||
return results
|
||||
|
||||
async def test_data_integration_service(self, user_id: int = 1) -> Dict[str, Any]:
|
||||
"""Test the data integration service."""
|
||||
logger.info(f"🔍 Testing data integration service for user {user_id}")
|
||||
|
||||
try:
|
||||
# Test the process_onboarding_data method
|
||||
integrated_data = await self.data_integration_service.process_onboarding_data(user_id, self.db_session)
|
||||
|
||||
if integrated_data:
|
||||
result = {
|
||||
'success': True,
|
||||
'has_website_analysis': bool(integrated_data.get('website_analysis')),
|
||||
'has_research_preferences': bool(integrated_data.get('research_preferences')),
|
||||
'has_api_keys_data': bool(integrated_data.get('api_keys_data')),
|
||||
'has_onboarding_session': bool(integrated_data.get('onboarding_session')),
|
||||
'data_quality': integrated_data.get('data_quality', {}),
|
||||
'processing_timestamp': integrated_data.get('processing_timestamp'),
|
||||
'context_keys': list(integrated_data.keys())
|
||||
}
|
||||
|
||||
logger.info(f"✅ Data integration successful for user {user_id}")
|
||||
logger.info(f" Website analysis: {result['has_website_analysis']}")
|
||||
logger.info(f" Research preferences: {result['has_research_preferences']}")
|
||||
logger.info(f" API keys: {result['has_api_keys_data']}")
|
||||
logger.info(f" Onboarding session: {result['has_onboarding_session']}")
|
||||
|
||||
return result
|
||||
else:
|
||||
logger.error(f"❌ Data integration returned None for user {user_id}")
|
||||
return {'success': False, 'error': 'No data returned'}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Data integration failed for user {user_id}: {e}")
|
||||
return {'success': False, 'error': str(e)}
|
||||
|
||||
async def test_ai_service_configuration(self) -> Dict[str, Any]:
|
||||
"""Test AI service configuration."""
|
||||
logger.info("🔍 Testing AI service configuration")
|
||||
|
||||
try:
|
||||
# Test basic AI service functionality
|
||||
test_prompt = "Generate a simple test response"
|
||||
test_schema = {
|
||||
"type": "OBJECT",
|
||||
"properties": {
|
||||
"test_field": {"type": "STRING", "description": "A test field"}
|
||||
},
|
||||
"required": ["test_field"]
|
||||
}
|
||||
|
||||
# Test the AI service manager
|
||||
result = await self.ai_manager.execute_structured_json_call(
|
||||
service_type="STRATEGIC_INTELLIGENCE",
|
||||
prompt=test_prompt,
|
||||
schema=test_schema
|
||||
)
|
||||
|
||||
if result and not result.get('error'):
|
||||
logger.info("✅ AI service configuration successful")
|
||||
return {
|
||||
'success': True,
|
||||
'ai_service_working': True,
|
||||
'test_response': result
|
||||
}
|
||||
else:
|
||||
logger.error(f"❌ AI service test failed: {result.get('error', 'Unknown error')}")
|
||||
return {
|
||||
'success': False,
|
||||
'ai_service_working': False,
|
||||
'error': result.get('error', 'Unknown error')
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ AI service configuration test failed: {e}")
|
||||
return {
|
||||
'success': False,
|
||||
'ai_service_working': False,
|
||||
'error': str(e)
|
||||
}
|
||||
|
||||
async def test_ai_structured_autofill(self, user_id: int = 1) -> Dict[str, Any]:
|
||||
"""Test the AI structured autofill service."""
|
||||
logger.info(f"🔍 Testing AI structured autofill for user {user_id}")
|
||||
|
||||
try:
|
||||
# First get the context
|
||||
integrated_data = await self.data_integration_service.process_onboarding_data(user_id, self.db_session)
|
||||
|
||||
if not integrated_data:
|
||||
logger.error(f"❌ No integrated data available for user {user_id}")
|
||||
return {'success': False, 'error': 'No integrated data available'}
|
||||
|
||||
# Test the AI structured autofill
|
||||
result = await self.ai_service.generate_autofill_fields(user_id, integrated_data)
|
||||
|
||||
if result:
|
||||
meta = result.get('meta', {})
|
||||
fields = result.get('fields', {})
|
||||
|
||||
test_result = {
|
||||
'success': True,
|
||||
'ai_used': meta.get('ai_used', False),
|
||||
'ai_overrides_count': meta.get('ai_overrides_count', 0),
|
||||
'success_rate': meta.get('success_rate', 0),
|
||||
'attempts': meta.get('attempts', 0),
|
||||
'missing_fields': meta.get('missing_fields', []),
|
||||
'fields_generated': len(fields),
|
||||
'sample_fields': list(fields.keys())[:5] if fields else []
|
||||
}
|
||||
|
||||
logger.info(f"✅ AI structured autofill test completed for user {user_id}")
|
||||
logger.info(f" AI used: {test_result['ai_used']}")
|
||||
logger.info(f" Fields generated: {test_result['fields_generated']}")
|
||||
logger.info(f" Success rate: {test_result['success_rate']:.1f}%")
|
||||
logger.info(f" Attempts: {test_result['attempts']}")
|
||||
|
||||
return test_result
|
||||
else:
|
||||
logger.error(f"❌ AI structured autofill returned None for user {user_id}")
|
||||
return {'success': False, 'error': 'No result returned'}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ AI structured autofill test failed for user {user_id}: {e}")
|
||||
return {'success': False, 'error': str(e)}
|
||||
|
||||
def print_summary(self, results: Dict[str, Any]):
|
||||
"""Print a summary of all test results."""
|
||||
logger.info("\n" + "="*80)
|
||||
logger.info("📊 ONBOARDING DATA VALIDATION SUMMARY")
|
||||
logger.info("="*80)
|
||||
|
||||
for test_name, result in results.items():
|
||||
logger.info(f"\n🔍 {test_name.upper()}:")
|
||||
if isinstance(result, dict):
|
||||
for key, value in result.items():
|
||||
if isinstance(value, dict):
|
||||
logger.info(f" {key}:")
|
||||
for sub_key, sub_value in value.items():
|
||||
logger.info(f" {sub_key}: {sub_value}")
|
||||
else:
|
||||
logger.info(f" {key}: {value}")
|
||||
else:
|
||||
logger.info(f" {result}")
|
||||
|
||||
logger.info("\n" + "="*80)
|
||||
|
||||
def cleanup(self):
|
||||
"""Clean up database session."""
|
||||
if self.db_session:
|
||||
self.db_session.close()
|
||||
|
||||
async def main():
|
||||
"""Main test function."""
|
||||
logger.info("🚀 Starting onboarding data validation tests")
|
||||
|
||||
validator = OnboardingDataValidator()
|
||||
|
||||
try:
|
||||
# Test database connection
|
||||
db_connected = validator.test_database_connection()
|
||||
if not db_connected:
|
||||
logger.error("❌ Cannot proceed without database connection")
|
||||
return
|
||||
|
||||
# Test user IDs to check
|
||||
test_user_ids = [1, 2, 3]
|
||||
|
||||
# Run all tests
|
||||
results = {
|
||||
'database_connection': db_connected,
|
||||
'onboarding_sessions': validator.check_onboarding_sessions(test_user_ids),
|
||||
'website_analysis': validator.check_website_analysis(test_user_ids),
|
||||
'research_preferences': validator.check_research_preferences(test_user_ids),
|
||||
'api_keys': validator.check_api_keys(test_user_ids),
|
||||
'data_integration': await validator.test_data_integration_service(1),
|
||||
'ai_service_config': await validator.test_ai_service_configuration(),
|
||||
'ai_structured_autofill': await validator.test_ai_structured_autofill(1)
|
||||
}
|
||||
|
||||
# Print summary
|
||||
validator.print_summary(results)
|
||||
|
||||
# Determine overall status
|
||||
overall_success = all([
|
||||
results['database_connection'],
|
||||
any(session.get('session_exists', False) for session in results['onboarding_sessions'].values()),
|
||||
results['data_integration']['success'],
|
||||
results['ai_service_config']['success']
|
||||
])
|
||||
|
||||
if overall_success:
|
||||
logger.info("✅ All critical tests passed!")
|
||||
else:
|
||||
logger.error("❌ Some critical tests failed!")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Test execution failed: {e}")
|
||||
finally:
|
||||
validator.cleanup()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user